Updates README

README.md

@@ -2,24 +2,32 @@
   VTON-IT: Virtual Try-On using Image Translation
 </h1>
 
+<div align="center">
+  <a target="_blank" href="https://arxiv.org/pdf/2310.04558.pdf"><b>Paper</b></a>&nbsp&nbsp |
+  <a href="https://github.com/shuntos/VITON-IT/">&nbsp&nbsp<b>Github</b></a>&nbsp&nbsp |
+  <a target="_blank" href="https://www.youtube.com/watch?v=sYdoLNQOzsk">&nbsp&nbsp<b>Video</b></a>
+</div>
 
-<p align="center">
+<br/>
+<p>
  This paper introduces VTON-IT, a novel Virtual Try-On application that uses semantic segmentation and a generative adversarial network to produce high-resolution, natural-looking images of clothes overlaid onto segmented body regions, addressing the challenges of body size, pose, and occlusions.
 </p>
 
+<p>
+Below is the overview of proposed VTON-IT. First, the human body is detected and cropped. Then, the desired body region is segmented through U2-Net architecture and the segmented mask is fed to the image translation network to generate wrapped cloth. Finally, the wrapped cloth is overlayed over the input image.
+</p>
 
-<div align="center">
-  <a href="https://github.com/shuntos/VITON-IT/"><b>Project Page</b></a> |
-  <a href="https://arxiv.org/pdf/2310.04558.pdf"><b>Paper</b></a> |
-  <a href="https://www.youtube.com/watch?v=sYdoLNQOzsk"><b>Video</b></a>
-</div>
-
+<p align="center">
+<img src="assets/overview.png" width=100%>
+</p>
 
+## Result
 
+For evaluating the performance of VTON-IT through visual observation, we compared the final overlayed images with the output of CP-VTON+. Image below shows that the proposed virtual try-on application produces more realistic and convincing results in terms of texture transfer quality and pose preservation.
 
-<div align="center">
-  <img src=final_overlay.jpg width="800">
-</div>
+<p align="center">
+<img src="assets/comparision.png" width=100%>
+</p>
 
 ## Requirements
 
@@ -30,29 +38,31 @@
 - opencv
 
 ## Training Pix2pix:
+
 ```
- python3 train.py --label_nc 0 --no_instance --name vd2.0_2  --dataroot ./datasets/vd2.0_2 --continue_train   --gpu_ids 0,1 --batchSize 2 
+ python3 train.py --label_nc 0 --no_instance --name vd2.0_2  --dataroot ./datasets/vd2.0_2 --continue_train   --gpu_ids 0,1 --batchSize 2
 ```
 
 ## Train Segmentation model
+
 ```
 u2net_train.py
 
 ```
 
-
 ## Inference
+
 ```
 Inference.py
 ```
 
 ## Reference
 
-If you find this repo helpful, please consider citing:
+If you find our paper and code useful in your research, please consider giving a star ⭐ and citation 📝 :)
 
 ```
 @misc{adhikari2023vtonit,
-      title={VTON-IT: Virtual Try-On using Image Translation}, 
+      title={VTON-IT: Virtual Try-On using Image Translation},
       author={Santosh Adhikari and Bishnu Bhusal and Prashant Ghimire and Anil Shrestha},
       year={2023},
       eprint={2310.04558},
@@ -62,10 +72,5 @@ If you find this repo helpful, please consider citing:
 ```
 
 ## Acknowledgements
- The authors would like to thank IKebana Solutions LLC for providing them with constant support for this research project.
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-
 
+The authors would like to thank IKebana Solutions LLC for providing them with constant support for this research project.